If your products and services don’t serve the data science community; however, you’re using data science in your products and services for a competitive advantage, you’re in a popular but challenging situation with your customers. I call what I’ve just described: using data science as a supporting strategy. For instance, the people at Graze.com incorporate data science into their snack business to develop and deliver the next box of goodies their customer will get. Let’s be clear though: they’re in the snack business, not the data science business. In this situation, I recommend keeping your data scientists as far away from your customers as possible. If you’re using big data as a supporting strategy, make it a priority to keep your customers insulated from your data science.

Buffering

Buffering is an important strategy for leaders using data science as a supporting strategy. In short, buffering is structuring at least one organizational layer between your data science team and your customers. Contrast this to leaders using data science as a core strategy–selling products and services to other data scientists, like RapidMiner, Kitenga (now part of Dell), and Cloudera. In this case, it’s a great idea to put your data science team in front of your customers, because like attracts like. However, Graze.com’s snackers have no interest in data science, so in this case, keep the analytics out of the conversation.

Instead, have your customers interface with other people in your company who are like them. The same “like attracts like” concept applies. If you’re in the business of wearables for athletic people, put a layer of athletic-minded people between your customers and your data science team. A good friend of mine is a triathlete that runs analytics to help other triathletes compete. Although he’s an analytic, he wears his triathlete persona when addressing his customers. Since he’s a one-man shop, that’s his only choice. In a larger company, this concept should obtain as a sales and marketing layer comprised of athletes–not engineers.

Translation

One important job of the buffering organization is to translate what the data scientists are trying to accomplish, into terms your customers understand. The reason why you don’t put data scientists in front of non-analytics, is that they’re typically difficult to relate to. Imagine a group of pro football players showing up at Comic-con. The first time a trekkie introduces themself to a linebacker in Klingon, there will be a problem. Before a product or service is introduced to your customers, it must be sanitized from its analytic underpinnings.

When Progressive talks to its clients about its SnapShot device, there’s no discussion about analytics. Their marketing may allude to the scientific prowess that goes into their product for effect; however, in practice they call it usage-based insurance. This is a perfect example of translation. Most drivers understand the term usage-based insurance. You’ll quickly lose them if you start talking about behavior-based digital profiling using a synthesis of regression and machine learning algorithms.

It may take multiple layers within the organization to successfully translate your analytic-based competitive advantage into customer-facing language. I’ve worked with several organizations where the developers are three or four levels removed from the customer. When I worked with Visa, there was a product development group, product function group, business analyst group, and then developers and architects. Sometimes it takes multiple translations to get it right for the customer.

Curating

Curating is a special requirement for those integrating advanced analytics into their products and services. A special challenge the buffering organization has with their analytic brain trust is information overload. Curating sifts through the piles of brilliance to extricate the golden nuggets that will appeal to your customers. That’s no easy feat.

Consider a museum curator whose job is to process archeological findings into a display of wonderment. Piles and piles of ancient bones, tools, and artifacts must be reduced, organized, and displayed in a way the appeals to the masses. Curators do more than just translate–they manage and oversee their body of work, and interact with the viewing public.

In a similar fashion, your curators must own the body of work produced by your data science team. Whether or not you put your curators in direct contact with your customer (both ways work), they should synthesize the wealth of information produced by your data scientists into a concise, attractive package that your customers will relate to. Even if you translate well, if you don’t curate, you’ll hit your target market with too much information and they’ll find a competitor that’s easier to understand.

Summary

There’s no doubt your data scientists are brilliant; however, too much brilliance for your uninitiated customers will drive them away. If you incorporate fancy analytics into your products, but your customers aren’t really jazzed by math and science, save the tech-speak for your in-house design team. As you structure your organization, ensure there’s a buffer between your data science team and your customers, who can translate and curate their findings. If I’m a Graze.com customer, I don’t want a lecture on how to design the perfect meal–I just want a snack.

Submitted for Publication in TechRepublic’s Big Data Analytics Blog

This is the sneak peak of my latest contribution to TechRepublic’s Big Data Analytics Blog. As editors do, when this gets published, some of the words and content may be arranged or deleted for a variety of reasons including SEO. What you’re looking at here is the uncut, unabridged, unedited version of the article that was submitted.

If you can’t get your data scientists and other analytics to be concise, you’ll never get anything done. To make the most effective use of your time, educate and coach your analytics on how to be concise.

As much as I love working with data scientists, this has to be the most frustrating part of my job. Analytic managers and consultants like me are responsible for getting things done; however, the very talented resources we deal with value brilliance over deadlines.

Notwithstanding their analytic disposition, everyone–including your data scientists–wants to succeed. To succeed, they’ll need to be concise: in their speech, in their writing, and even in their approach to solving difficult problems. Here are my seven best tips for making that happen.

Tip # 1: Analyze the Impact

You must do your homework before you broach the subject of concision. At the onset concision is very uncomfortable with analytics, so they’ll need to rationalize it for themselves before they unseat their de facto behavior. So when in Rome, do as the Romans. Do some research on the benefits of concision and the costs of not being concise, and prepare some analysis.

In my experience, you can double or triple your productivity, when your team effectively practices concise behaviors. You’ll need the numbers for your specific situation to make it relevant. High-level studies are interesting, but when the analysis is brought into their reality, it becomes impactful.

Tip # 2: Communicate the Need

Armed with your analysis, you must let them know what your intentions are. You can do this formally or informally, depending on the structure of your organization. I like informal–it’s better for engagement; however, do whatever you feel works best. Double- and triple-check your analysis; remember, you’re dealing with people who can spot a hole in your analysis a mile away.

This should be an engagement, not a communication. Engagement implies dialog and discussion. Listen to what they have to say: their feedback and concerns. Make them understand that you understand. If they don’t voice any concerns, they’re either not listening or not internalizing the implications of the message. Continue the dialog until they stop head-nodding and start sharing.

Tip # 3: Teach Them How

Concision is a skill that needs to be taught. Work with your team coach, Human Resources, or an external consultant to design a program that teaches concision. The facilitator should be familiar working with analytics—they are a special breed when it comes to this type of instructional design. Analytics have always been good at whatever they try to learn; you’re asking them to learn something they won’t initially be good at. It takes finesse to navigate through this human dynamic.

Tip # 4: Show Them How

Modeled behavior should follow education. Once your analytics have some guidelines to ponder, they’ll want to see it modeled in exemplars. The analytic manager on a data science team should be the paragon of concise behavior. Shorten one hour working sessions to thirty minutes and eliminate status meetings altogether. When documents are created or reviewed, focus on communicating the most amount of information in the least amount of space, with the question, point, or thesis within the first few sentences.

Tip # 5: Help Them Build

Be encouraging and supportive, not critical or condescending. Analytics are especially sensitive to skills they can’t quickly master. Give them time to grow and they’ll eventually come around. In addition to modeling concise behavior, I suggest introducing them to a well-written newspaper like the New York Times or the Wall Street Journal. I receive regular email alerts from the Wall Street Journal. They’re usually a hundred words or less do a great job of communicating breaking news within a few seconds.

Tip # 6: Give Them Feedback

Give them positive feedback when concision is done right. They won’t do it right for some time, so here’s where you have to be very careful. Criticizing an analytic for rambling or producing a tome when a brief will suffice, is a natural tendency that should be avoided. Even when it’s in the spirit of improvement, highlighting any shortcomings should be done with care. In this situation, just ask them to produce a more concise version, and be specific. I once had a data scientist give me a 50-page PowerPoint of all words. My feedback was that it had a lot of great content, but I’d need a 2-page process visual to call it done.

Tip # 7: Give Them Kudos

When you see your analytics exhibit concise behavior, whether a brief response or a quick turnaround on a priority deliverable, make a big deal out of it. Constructive feedback should always be done in private, but exemplary behavior should be well publicized. Leaders should support analytic managers in this effort; even a handshake from a higher-up is a big deal to most people. Everyone appreciates kudos, but more importantly when analytics see their peers getting rewarded, they take notice.

Summary

Be concise, and coach your analytics to do the same–enough said.

Submitted for Publication in TechRepublic’s Big Data Analytics Blog

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I'm sure you are; it's human nature. A client recently told me that she came home one day and noticed that there was a water-filled glass sitting directly on a wood table. She asked her husband, "Where is the coaster for this glass?" Her husband responded, "That's what you noticed? I just finished cleaning the entire house!"

I see a lot of leaders frustrated with their data science team. They've spent a lot of money so the have very high expectations. In consulting, we call that White Knight Syndrome, and I deal with it all the time. So when things don’t go as expected, they go down a very classic route of identifying gaps and solving problems. Not only is this enervating, but it's a reckless abuse of your data science team's potential. It's far better to build on the strengths of your data science team, than it is to improve on their weaknesses. Here are five things to absolutely love about your data scientists.

They Fuel An Uncatchable Competitive Advantage

Your data science team is a key ingredient for a breakthrough competitive advantage. This is no joke; so don't ever overlook this fact. They tackle unsolvable problems for fun, in a way no other profession can. Most people take for granted how the data scientists at Google have changed the world, with a search engine that was late to the party. Sure, the leaders had the vision that powerful search capabilities would equate to market domination; however, it was the data scientists that figured out to jump into our brains, figure out what we were trying to find, and bring back the most relevant results. Google's data scientists made it one of the most powerful organizations in the world.

They're A+ Students In School and Life

Data scientists learn fast and retain extremely well. They've done it their whole lives. Most data scientists you encounter excelled in school—4.0 GPA in high school and college. And although you would expect them to get good grades in computer science and math, remember that a computer science degree has more than just computer science classes. Data scientists don't only get good grades in math and science; they get good grades in everything. Don't be shy about bringing them into your business world. They'll start contributing real value faster than you realize.

They Deliver No Matter What

Data scientists are extremely loyal under the right conditions--sometimes to a fault. I can't count the number of times I've been roped into an all-nighter because of situations far out of my control. We dig in and we deliver anyway; it's part of that excellence gene that I referenced earlier. The only thing you need to do is setup the right conditions, which has more to do with job satisfaction than money (although a good paycheck doesn't hurt either). Data scientists love to create data masterpieces with people they enjoy. With the right environment and the right challenge, they'll stay with you all the way.

They Are A Magnet For Other Talent

It seems like everybody's having a hard time finding good data scientists, except for other data scientists. If you're a leader, you probably know a lot of other leaders; so, guess who data scientists hang out with? You guessed it--other data scientists. This is important to you on a number of levels. If you ever need to extend your team, the best source for finding more data scientists is the team you already have. Also, the data scientist community is very supportive. So if your team actually gets stuck on a problem, there's a huge brain trust at their disposal that's ready and willing the help.

They Save Your From Yourself

Data scientists think through everything before making a decision. This will and should drive you crazy if you're an impulsive leader. Impulse is good for immediate action, but like all things the best results come from Aristotle's golden mean--the desirable middle between two extremes. At one extreme is a knee-jerk reaction that gets you into trouble (sound familiar?) and at the other extreme is analysis paralysis. The trick is to get the right balance, and you won't do that without the counsel and reason of your data scientists. You may think you have a good idea, but it won't sit right with your data scientists until there's data, research, and analysis. This voice will save your assets more times than not.

Summary

Identifying problems and closing gaps with your data science team will only bring you status quo; however, identifying strengths and raising the bar will catapult you to a place nobody can catch. Instead of obsessing about what's wrong; invigorate your organization by using the strengths within your data science team. There's a lot to love: they're extremely bright, loyal, and precise. Make this your starting point and enjoy your immaculate house, instead of worrying about a missing coaster.

Submitted for Publication in TechRepublic’s Big Data Analytics Blog

This is the sneak peak of my latest contribution to TechRepublic’s Big Data Analytics Blog. As editors do, when this gets published, some of the words and content may be arranged or deleted for a variety of reasons including SEO. What you’re looking at here is the uncut, unabridged, unedited version of the article that was submitted.

Does it feel like you're spinning on your next big product idea instead of moving forward? That's a very expensive scenario when a data science team is involved.

I'm often called into companies to organize them and move them forward. Most of the time, they have an idea of what they want to do, but for some reason, they just can't move things forward. There's a lot of activity, and a lot of meetings, but no real accomplishments. Does this sound familiar?

There are several reasons why this happens, but all comes back to execution excellence, which is not intuitive or intentionally developed as a capability in most organizations. Even with great thinkers and doers, if you don't have a good frame for moving an idea into action, you'll probably spin. However, if you're focused and organized, your data science team can begin work on your next big idea in just five days.

It Starts With Leadership

The first day starts with you--the leader. If your organization is spinning around, my guess is that you're trying to get too many things done at once. If your next big idea is really important, your first job is to decide that it takes priority over everything else. You must resolve this for yourself before engaging with the rest of the team.

Once you've resolved that this is where your organization will focus, develop logical and emotional reasons why everyone should make the development of this product their priority. I had a leader tell me if they don't differentiate somehow, they're going to die. That's compelling and emotional! This is the message that you want to move forward with.

Start With the End In Mind

On day two, in the spirit of the advice given to us by the late Dr. Stephen Covey, start with the end in mind. Define what success looks like with your leadership team. This can take an hour or it can take all day--but it shouldn't take more than a day. The outcome of this exercise is more than a vision statement; it's a vivid depiction of how the future will look. I recommend doing this in three cycles: macro-environment, competitive environment, and internal environment; in that order.

In the first cycle, paint an outline of your future macro-environment, including political, economic, social, technological, environmental, legal, and other factors that affect your company. Fill in this outline on the second cycle with your competitive environment, including: customer, suppliers, new entrants, and alternative offerings. Finally, complete the vision on the third cycle with how your organization will look, including size, composition, culture.

You've Got The Brains, Now Start Storming

On day three, involve your entire data science team in a brainstorm. The goal is to understand how the team will achieve the vision. The pre-work on days one and two are important. Open the meeting with the logical and emotional reasons why this effort is more important than anything else they're working on and clearly articulate your vision.

During your brainstorm, let the ideas flow. Encourage free flow of thought, and capture ideas in an organic fashion (in a mind mapping tool) and not in a linear fashion. Most brainstorms like this will last a few hours, so make sure to incorporate breaks. When I reach most organizations, they've started here and they're stuck here because nobody's defined a cutoff period. You're cutoff period is the end of the workday--after day three, there will be no more brainstorming.

Making Sense Of It All

Bring the team back on day four to organize everything. It's important to reinforce the sequence--we're done with guidance, we're done with visioning, and we're done with brainstorming. Don't let the team regress at this point--that's how everything goes circular. The team must mentally switch modes from brainstorm to organize.

Organizing is about grouping and removing duplicates. This can be time consuming for some; however, it’s easier for data scientists. They are naturally adept at separating ideas into affinity groups. You should reduce the ideas in your brainstorm into tangible deliverables; this will be the basis for your action plan. One more day to go.

Moving Forward

Bring everybody back on day five to build an action plan. Set the expectation that by the end of the day, work will begin. Divide the day into two parts. The first part of the day is spent identifying the top priority deliverables (from the action plan) and when they will be done.

The second half of the day is a working session to get started on the top priority deliverable. While the data scientists are moving forward, the analytic manager completes the action plan and the change leader is starts on the stakeholder map. If you want to move forward within five days, schedule it into the agenda for day five.

Summary

If you have a great idea, and you have a data science team, you should be getting things done and not meeting to schedule more meetings. I've given you a simple, five-day agenda for moving forward. It starts with a resolution you make with the man in the mirror--so take that first step. If everything's a priority then nothing's a priority. Make this the priority, and in five days you'll be well on your way to the next level.

Submitted for Publication in TechRepublic’s Big Data Analytics Blog

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If you're creating a product or service that incorporates data science and big data analytics, you might be paying too much attention to artificial intelligence and not enough attention to superficial intelligence. Data science is filled with mystical algorithms reminiscent of spells chanted by wizards of yore. Armed with this arsenal of prestidigitation, zealous leaders eagerly present their market with new and improved widgets, powered by artificial intelligence. However, many times they take an egocentric view of the world, relying myopically on their internal capabilities for advanced analytics. If you flip this around to a customer-centric view, you'll see intelligence doesn't need to be artificial to be valuable. To get the most value from your artificial intelligence application, combine it with the superficial intelligence obtained by involved communities.

The Wisdom Of The Crowd

There's a wealth of valuable data available in plain sight and happening right now--I call this superficial intelligence. When I was in grade school, my neighborhood friends and I would occasionally start a pickup football game in the middle of the street. We would post the girls on the corner to signal us when a car was coming, so we could move out of the street. This was great superficial intelligence for us. Without the benefit of this information, a wide receiver might be tackled by an unwelcome, automotive defensive back!

Superficial intelligence is a great addition to your bag of data science tricks, as it adds to your existing base of artificial intelligence and it represents a more customer-centric marketing approach. This primarily applies to leaders who are using big data analytics to support their core products and/or services: similar to Progressive Insurance's Snapshot device, where analytics supports a traditional product (insurance) to gain a competitive advantage. The value of data and information doesn't need to be artificial or involve sophisticated analyses to be valuable. Just knowing that a car was turning down our street was great to know. Where this starts to get exciting for data scientists is when you combine superficial intelligence with artificial intelligence. That will take your game to whole new level.

A great example of this is an application I just downloaded on my iPhone called Waze. If you haven't heard of it yet, you really should. Like Google Maps or MapQuest, Waze is an application that helps you navigate the streets of your locale. You give it an address, mount your phone in your car, and it gives your real-time navigation instructions to your destination. What's different about Waze though, is the Waze community, which is actively involved in feeding you superficial data. For instance, with the help of your local community, Waze tells you where there's an accident, construction that requires a detour, or even a cop hiding out under a bridge. Waze combines this information with real-time analytics to determine your best route. It's amazingly powerful and accurate. I don't say this often, but it actually puts Google to shame. That's what the wisdom of the crowd can do for you.

The Human Machine Synergy

To apply this principle of combining artificial and superficial intelligence, consider the evolution of data into wisdom. I'd say superficial intelligence gives you a good base of data to start with. Remember, data is just raw, uncultured insights. If there's an accident a half-mile away or a car around the corner, that's really good data that someone could use. You can combine this with non-crowd-sourced data. Waze obviously has geographic data at its immediate disposal and I'm sure the team at Waze curates of wealth of other information as well. This data becomes useful when it evolves into information.

Information is analyzed and applied data. When Waze analyzes all the stock and superficial data coming from the Waze community and tells you to "turn right," that's information. Information tells your consumer what to do with all this data, based on their objectives. So again, you must transcend the pure data paradigm and think about what your customers might be trying to accomplish. Then, using a mix of base data and superficial data, perform a real-time, big data analysis to prescribe their next step. This strategy alone puts you at a distinctive advantage, but there is one more level you can take it to.

Information evolves into knowledge, which further evolves into wisdom. Knowledge is when you take information from disparate sources and combine them for new insights. With superficial intelligence, you're already going down this path; however, for more impact, you'll want to explore related but very different sources of information. I used to live next to an arcade, which would sometimes host special events that drew a lot of traffic. So, it wouldn't be a good idea for a pickup game on one of these days due to the traffic. Wisdom comes from maturing knowledge over time. The first time we tried a pickup game at 5p when everyone was coming home from work, we learned our lesson. If you apply these ideas to your next product or service, you will probably be approaching breakthrough territory.

Summary

Artificial intelligence is great, but when combined with the superficial intelligence of the crowd, your product or service goes to a whole new level. Take some time to consider how your existing data can benefit from additional, crowd-sourced data, and what your analytics would look like at that point. Then, survey your customers and see if they would be willing to form a community around your offering. With the wisdom of the crowd on your side, you can't go wrong.

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After scanning this list, I’d say these companies are pretty successful. And although it’s debatable which came first—success or happy employees—most of the extant literature on organizational development confirms the great contribution that happy employees make to successful companies. Personally, I’ve worked with many companies over my illustrious career as a consultant, and I can attest that I’ve made far more valuable contributions to companies when I was having fun.

Happiness works differently for various people; however, I can give you some insights when it comes to analytics: data scientists, analysts, and most IT professionals:

Smart people like working with other smart people.

I had lunch last week with Sridhar, a good friend of mine who currently manages a team of IT professionals at StubHub. We were previously partners in crime at Hitachi Data Systems and together we solved some of its difficult challenges. At one point during the lunch he said, “I just like solving problems with other smart people.” The comment struck a chord with me that stayed for a while.

It was harmonious with a discussion I had earlier with Jennifer Selby Long, a brilliant management consultant who develops leaders. Jennifer and I are looking to pair up on an estimable intervention at a local telecommunications company. At one point while working through the proposal she said, “This one may be tough, but I just love working on difficult problems with other smart people.”

Of course there are other factors that contribute to employee happiness; however, for smart people, this is an important one. Make sure to surround your smartest people with other smart people. Oh—and a huge salary doesn’t hurt either.

Happy Tuesday, folks! I hope everyone had a fun and relaxing Memorial Day weekend. I sure did; Kim and I lounged around all weekend, usually doing something close to nothing. If you know me at all, you know cooking was on the agenda; however, this year I went a little mellow compared to some of the other holidays. I need to drop some pounds, so I started on Atkins a few weeks back. It’s going pretty well so far; I’m down about 7 or 8 pounds. One adjustment that I thought was going to be tough was eating more vegetables; however, it has pleasantly turned out well. The key I’ve found is this: the right ingredients make all the difference—that’s true with diets and it’s true with strategy.

One thing I’ve really grown to love is tomatoes. Before starting the diet, I would rarely eat tomatoes; however, now I eat two to three every day. And now that I’m a tomato connoisseur, I’ve noticed that not all tomatoes are created equal. Sure, Roma tomatoes will not taste like Beefsteak tomatoes. What matters more though is where I get the tomatoes from. The tomatoes from Safeway aren’t as good as the same type of tomatoes from Whole Foods; and these aren’t as good as the same type of tomatoes from Windmill Farms (they carry a lot of fresh produce from local farmers). The tomatoes on the vine at Windmill Farms are awesome and the same type of tomatoes from Safeway are barely okay, even though they look similar.

Selecting people for your strategy—whether they’re full-time employees or consultants—is like selecting tomatoes. The talent differential between average, good, and great is sizable; and looks can be deceiving. It astounds me every time I come across someone from a big-name firm like McKinsey, Deloitte, or Accenture, who doesn’t know the difference between strategy and long-range planning. Or, an Executive Vice President with an MBA who cannot make a decision.

One of the critical elements of executing a successful strategy, is making sure you have the right people on your team. Selection is crucial—you don’t want to end up with a bunch of sour tomatoes.

I had power issues again this weekend. No, I wasn’t suffering through one of San Ramon’s infamous blackouts, this time I was dealing with the power in my car—the battery was dead. And, unfortunately, the battery charger was no help this time, because the battery was completely dead—flatlined. This happened because nobody drives that car, and as you may know, a car that stays idle for too long will have performance problems when you call on it to work. The same is true for data scientists on your big data team—you must keep them busy solving important problems.

First of all, data scientists are very expensive resources, so it’s just irresponsible to hire a few, just to have them watering the plants while you figure out what you want to do with them. More importantly, idle data scientists need to stay busy with challenging and exciting work, or they’ll lose enthusiasm for what you’re trying to accomplish. And if this period of inactivity is extended, it’s hard to engender urgency when it’s time to get serious.

This is a bigger responsibility than you might expect. It’s common for me to see idle data scientists while the leadership struggles to get their plans in place. This is a very bad situation. Thoroughbred horses are bred to run, and if you don’t keep them moving, they’ll lose their edge. Data scientists are a particular breed of analyst—not unlike a thoroughbred. Some business analysts are okay with just a moderate amount of activity, but data scientists thrive on solving problems, and get distracted and demoralized when they don’t have a big problem to solve. In the same way you must keep a thoroughbred moving, or a car running, you must keep a data scientist analyzing, or they’ll lose their edge.

The only thing left to do with my car this weekend was to call AAA and have them replace the battery. You don’t want to get into a situation where your big data’s power source needs to be replaced. Make sure they always have something important to do.

Banks need an overhaul in their lending practices and I think big data can help. There’s little chance I can get a bank loan right now (not that I need one) even though I’m probably one of the lowest risks in the country. I say that because I emerged from the financial meltdown of 2008 without missing a single payment on anything: loans, credit cards, office rent—even the gardener got paid on time. Compare that to all the FICO superstars that collapsed after two months of no work. Consumer behavior is not easy to model, but if your business relies on it, you better be good at it.

Banks lost a tremendous amount of money because they relied on dubious and ineffective scoring models and now they’re not sure who to lend to. This is bad news for banks—lending money is how they stay in business. I never understood why lending institutions—with all their core competence in analysis—would rely so heavily on FICO scores and lightweight scoring instruments. For instance, I can’t understand how two years of tax returns demonstrates your ability to pay on a 30-year mortgage; however, this still seems to be the gold standard for income verification. And don’t get me started on FICO; I’ve seen my credit score swing 121 points over the last five year period. First, they say I’m a very high risk—then they say I’m a very low risk. All the while, I really haven’t changed a bit.

My advice for banks is to bring their core competence for understanding consumer behavior in-house and reinvent their lending model. Big data and predictive analytics are in a place right now where very sophisticated modeling can be done on consumer behavior. Throw away the arbitrary rules of thumb and forget about FICO—it’s not effective. And even if a new, fancy consumer behavior modeling company opened its doors, why would you outsource something that’s so important to your survival?

Exigent innovation is painful; however, what’s the alternative? The good news for banks is that big data presents an opportunity to pull out of this mess. The question is whether they see it.

Blackouts remind me why I’m not a devout agilist anymore. My block in San Ramon had another one if its infamous blackouts a few days back. And just like the last major blackout we had, my productivity only decreased a tiny bit; I was certainly able to accomplish all the critical things on my plate, including an important client deliverable that was due that day. At worst, blackouts are a nuisance for me; they don’t really slow me down because I’m prepared. However, that wasn’t the case in the early part of this century when I was a born-again agilist. When it comes to execution, Aristotle had it right with his golden mean—you must find a good balance between the two extremes of classical waterfall and agile.

The ideology of agile management proscribes advanced planning, even when it comes to risk management. The way agilists handle risk—like everything else—is very empirically. In agile execution, there’s the concept of yesterday’s weather wherein the belief is that today—for all intents and purposes—will be like yesterday. So, instead of formally analyzing risk, agilists just assume they’ll crank out as many widgets in this cycle as they did in the last cycle. Axioms like this allow them to forego much of the upfront planning that classic (i.e. waterfall) managers would necessarily undertake. That’s all fine and good—until it’s not fine and good.

The reality is this: Murphy is far too mischievous to be that consistent. Tuesday I had power all day long, just like Monday, Sunday, and Saturday. However, on Wednesday I had no power from about 6:00pm until about 3:00am the next morning. That’s nine long, dark hours if you’re not prepared. This is one big area where agile execution simply falls short—I don’t care how passionate you are about the ideas.

Fortunately for me—long before Wednesday—I stocked up on candles, lanterns, tap-lights, flashlights, portable light-bulbs, and most importantly batteries of all shapes and sizes. Kim and I actually had a pretty nice time that night after I finished up my work. The house was well lit, we ordered some food from a local restaurant, and we watched TV together on the iPad by candlelight (and battery-powered lanterns).

Don’t get me wrong; I’m still very much in the agile camp for most scenarios. However, like Aristotle would probably say today, you cannot be a die-hard fanatic on one style or methodology. That’s why the consultant’s standard answer to any question is, “it depends.”

Don’t let your zeal for agile put you in a blackout without batteries.

We planted some beautiful Hibiscuses (Hibisci?) earlier this year and with the amazing spring that we are experiencing here in Silicon Valley, the blooms are just enchanting. As a Hawaiian, these flowers have a special meaning for me—they are the state flower of Hawaii (okay, the state flower of Hawaii is actually the Yellow Hibiscus, but these are close enough). Now, when I step outside, I’m often transported to my favorite spot on on Waikiki beach, the house in Pearl City where I lived with my family as a teen, or the beautiful grounds of the Hyatt in Maui where my wife and I were married. This all comes from a simple flower. Symbols have the power to be transcendent in the message they convey to your big data strategy team.

Symbols are objects, acts, or events that convey a special meaning. I was talking with a colleague yesterday who did some consulting for Apple, and she mentioned that contractors and consultants had badges with muted, grey apples; whereas, all the employees had bright, colorful apples. This and other rituals made her feel like an outsider.

Along with rituals, stories, and the infamous grapevine; symbols are a component of your organization’s informal system. Your informal system exists whether you like it or not and has greater power than your formal system (e.g. mission, vision, stated policies) to influence the people in your organization.

As a leader, the most important thing you can communicate to your big data strategy team—both formally and informally—is your support. What symbols do you have in place for this? Here are some questions to help you figure that out:

Do you hand out special awards to them?

Do you have special ceremonies for them?

When they walk around campus is their status on the big data strategy team conspicuous and respected?

Does your office layout make you accessible?

Do they feel comfortable approaching you for help?

The symbolism in your company is working for you or against you. It’s up to you to figure out which force is in play and make adjustments if necessary.

If you’re invested in gold, you may be experiencing a bit of anxiety. The precipice gold fell off this month makes the fiscal cliff look like a street curb. At the beginning of the month, an ounce of gold was worth about $1,600, and now it’s struggling to stay above $1,400. That’s a tough reality for someone who was recently worried about the US dollar and turned to gold for a safe place to invest. Unfortunately, there are a lot of people investing in gold based on sophistic reasoning. It’s important to check with trusted advisors before making important decisions.

The dirty truth is that people in the inside think most gold investors are pretty ignorant. They’re saying that dumb money is pushing up the price of gold—and I believe they’re right. Although common lore says that gold is a good investment to hedge against the US dollar, it’s a trading instrument that works like anything else. When gold is popular—for whatever reason—people buy it and the price goes up. When people get scared, they sell their gold, and the price goes down. If a lot of people get scared at the same time, they all sell at the same time, and the price plummets. This is what happened a few days ago.

The key here is that most financial analysts knew this—the joke is on the gold investor who didn’t know better. It’s important to surround yourself with trusted experts and turn to them before you make any big moves. Otherwise, you might end up holding fool’s gold.

It’s Sunday night and I’m relaxing with Kim: indulging on our favorite weekend past-time—reality TV. My new favorite reality show is Bar Rescue. If you watch Bar Rescue and you know anything about me, that shouldn’t come as a big surprise. Bar Rescue features Jon Taffer, a veteran in the nightclub and bar industry, who specializes in turning around bars in a nosedive. Each episode chronicles Taffer’s attempt to save a bar that’s heading for disaster. Here are 9 things I love about Taffer and his approach:

Taffer uses both bar science and common sense in his interventions.

Taffer doesn’t intervene until the bar owner asks him for help.

Taffer always starts by observing the situation with his own eyes.

Taffer has been involved in hundreds and hundreds of bar ventures—experience matters.

With all the high-tech, analytic software tools that I play with on a daily basis, one of my favorite tools is actually my shoe horn.

It is a gorgeous day in Silicon Valley today, and I’ll be spending part of it at lunch with a good friend Julian, who I met when I was consulting for PayPal. As I’m getting dressed, I realized how often I use my shoe horn, and it made me think about why this is a great tool. Great tools are simple.

I see a lot of companies choosing the wrong tools for their strategy. I’m one of the key contributors on TechRepublic’s Big Data Analytics blog, and someone made a comment the other day on one of my posts indicating that executives are erroneously trying to use Big Data to solve everything. He’s absolutely right; I see the same thing. It’s a very expensive mistake; Big Data resources are not cheap.

The bigger problem with most Big Data tools is that they’re complex. Sure, data scientists know what’s going on, but from the executive perspective, it seems like an alligator that you need to feed with fancy technology and fancy people. This isn’t good.

In fact, in many cases, you can get to where you want to go without big data. Most companies don’t even have their small data under control. And even if it makes sense to use big data for your strategy, you don’t need to dive straight into the deep end of complexities that you don’t understand.

If at all possible, keep your tools simple. Sure, I can hire a team of professionals to design a fancy, electronic device that will get my shoes on in sub-second time—but I’ll just stick with a shoe horn.

Because of my experience and credentials as a Six Sigma Black Belt, I’m often called into companies to help them improve a process that shouldn’t be improved. I was recently on a Six Sigma effort where the process was so broken we couldn’t even establish a baseline. That’s a good clue that you’re heading down the wrong path. You cannot improve a defective process—you need to replace it.

Most people identify Six Sigma with process improvement (i.e., DMAIC[1]); however, there is another part of Six Sigma that deals with process development (i.e., DMADV[2]) called Design for Six Sigma, or DFSS[3] for short. Although the two look similar side-by-side, the execution is very different. For instance, both have a measure phase following their design phase; however, with DMAIC a key goal of the measure phase is a baseline; however, with DMADV that goal doesn’t exist. Instead, you’ll focus more on obtaining a crisper understanding of how the new process will be measured.

To decide which path to pursue, ask yourself whether you have an efficiency problem or an effectiveness problem. For instance, if the process works okay; however, the results are coming out too slow, you have an efficiency problem that requires DMAIC. If however, the process doesn’t work at all, you have an effectiveness problem that requires DMADV, which is more along the lines of process innovation.

Trying to improve a dysfunctional process is like changing the oil in a blown engine. It doesn’t make any sense. Before you start a process improvement effort, make sure you first have a process to improve. If you don’t, it’s best to just start over with a new process.

DMAIC stands for Define, Measure, Analyze, Improve, Control; and represents the major phases of a Six Sigma process improvement effort. ↩

DMADV stands for Define Measure, Analyze, Design, Verify; and represents the major phases of a Six Sigma process development effort. ↩

For most intents and purposes DMADV and DFSS can be used interchangeably to represent process development using Six Sigma techniques. For those who care, DFSS is more of objective-based characterization and DMADV is more of a process-based characterization. ↩

Well, it depends; let’s first understand why you feel light bulbs are necessary.

(I’m kidding)

Actually, I had a light bulb moment yesterday—literally. We have a small chandelier in our entry way that blew its last bulb this past weekend, so my first order of business was to shed light on the situation (pun intended). Once I got up on the ladder, I realized I had a situation. I could not reach the light bulbs because there was a grey, metal diffuser in the way. It’s there so that people upstairs looking down don’t get blinded by staring directly into the bulbs. The only solution that came to mind was to remove the large, heavy, glass base of the contraption. So that’s what I did.

Before long, I was screaming to my wife for help. I’m balancing on the third step of a ladder holding a heavy, delicate ornament in one hand and the knobs that hold it in place in the other. Fortunately, Kim quickly came to the rescue and I was able to change out the light bulbs without breaking my neck.

Later that day, I stopped into the lighting store where we bought the chandelier and told my story to the owner. He patiently waited for me to finish my story, smiled, paused, then explained to me that I should have removed the diffuser—not the huge glass bowl at the bottom.

Good information not only increases strategic effectiveness and efficiency, but it also reduces risk. I talked about this yesterday when I was commenting on the awful bombings at the Boston Marathon. In my chandelier episode yesterday, I got the result I was looking for—light where there was no light. However, I could have arrived at the same result with much less risk, had I known about removing the diffuser instead of the base.

I’m still struggling to process how anyone thinks it’s okay to set off a bomb in the middle of a crowd of innocent people over a difference of ideals. I was in the dentist’s chair this afternoon when my wife sent me a text succinctly detailing the awful Boston Marathon bombing. I couldn’t believe it—and still can’t. It’s unfortunate that a plot like this actually succeeds; however, I’m thankful for all the terrorist plots against our people that don’t. Although I talk a lot about using information for strategy and innovation, information prowess is also a powerful tool to mitigate critical risks.

It’s hard to notice non-events because they aren’t conspicuous; however, it’s remarkable to think about all the terrorist plots that were attempted and failed. Our intelligence agencies work with our enforcement agencies around the clock to monitor and intercept all the crazy schemes devised to harm and kill Americans. At times like this, President Obama reminds us, our friends, and our enemies how serious we are about justice around these matters. The combination of leadership and information prowess keeps critical risks from surfacing. The unfortunate event in Boston today is the exception that makes the rule.

All strategy is vulnerable to the effects of critical risks—not only those that involve Big Data or some other form of information exploitation. Your degree of analytic capability has a direct impact on how well you mitigate these risks. You can see this in action with Santam, South Africa’s largest short-term insurance provider. With big data and predictive analytics, Santam was able to save millions that were previously lost to insurance fraud.

Mitigating critical risks is an important part of any leader’s strategy. If the stakes are high enough, it may make sense to assemble a big data team for the sole purpose of making sure nothing happens. Regardless, take some time today to see where advanced analytics might neutralize your biggest risks.

My sincere condolences to those affected by the Boston Marathon bombings

My wife just bought me a great cookbook by Faye Porter called, At My Grandmother’s Table: Heartwarming Stories & Cherished Recipes from the South. When I’m not out helping executives turn their chaotic data into strategic wisdom, I’m often found in the kitchen or the backyard cookin’ up something good. I love to cook and I like to experiment with new ideas; but I absolutely love old-fashioned cooking (methods and recipes). Although you must always be innovating, some of the best ideas come from the wisdom that precedes us.

For instance, look at the way I define Big Data for use in a competitive business strategy:

Big data is the massive amount of rapidly moving and freely available data that potentially serves a valuable and unique need in the marketplace, but is extremely expensive and difficult to mine by traditional means

I opened up TechRepublic’s Big Data Analytics Blog with my seminal post, Big Data defined, wherein I systematically explained this definition using the underpinnings of Michael Porter’s five forces analysis. Although Porter put out these ideas in the 1980s, they’re still relevant for academic discussions on strategy and for evolutionary derivatives as I did with defining Big Data for strategic competitive reasons.

With all the charm brought about by the novelty of Big Data, it’s easy to lose sight of the the past—this is a mistake. We have a wonderful repository leadership and management theories and ideas that date back to Taylorism in the early 1900s: and all the way back to the history of time if you study leaders qualitatively. Bringing these ideas current is a talent you should embrace.

A few hours in Fry’s Electronics gives me powers beyond belief. I spent the whole day yesterday meeting with clients in Campbell and Sunnyvale. An unexpected call at around 4:00 PM put me on the 880 Freeway at around 5:00 PM when it’s in parking lot status. After inching my way to Mission Boulevard, I decided to stop off at Fry’s Electronics—my Fortress of Solitude. I entered tired and weary from a long day of meetings and emerged with the vigor to conquer Mount Everest (okay, maybe I’ll start with Mount Diablo). Burnout drains talent; understanding how to recharge your analytic team is vital to getting the most from them—both in productivity and loyalty.

Like most analytics, I’m an introvert (INFJ for those who understand what this means). If you lead and/or manage a team of analytics, it’s important to understand how introverts work. There are many misconceptions. Contrary to popular belief, introverts like being in social settings, have no problem voicing their opinion, typically have a great sense of humor, and can be very fun to hang out with.

The accurate distinction between introverts and extroverts is where their locus of energy lies. Introverts revitalize when they’re alone. They’ll function fine in a social setting; however, their battery is draining quicker than extroverts. If you put them in meetings all day or extended team-building exercises, they will quickly burn out.

To protect your analytic team from burnout, schedule downtime for them: especially on the heels of extended and extensive social interaction. A field trip to Fry’s Electronics from time to time might not be a bad idea either.

Are you getting enough iron in your leadership diet? In deference to the recently departed, and former UK Prime Minister, Margaret Thatcher, I’d like to address leading with an iron fist. Thatcher personified this idea so well, she leaves behind a legacy of being the “Iron Lady.” Forget about individual body parts, to many her whole being was iron! She’s known and remembered for her convictions—something that I see critically absent in today’s leadership.

I blame the sociologists who study leadership for this phenomenon. The recent trend in leadership is servant leadership where leaders are advised to become a servant to their followers. It’s especially rampant out here in Silicon Valley, where nothing gets decided until everyone—including the janitor—is okay with the decision. This is nonsense. Not only does it take way too much time, but it’s simply not effective.

If you need to make a decision, you don’t need a committee or a Kumbayah session with your group. Just get some good information. Information can help you stay your ground when your convictions are being challenged. Many times analysis contradicts conventional wisdom, allowing you to draw insightful but controversial assumptions. If your hunch is validated by data analysis, many naysayers will just refute your analysis—it’s not for them, it’s for you. You must believe in your decisions—and that takes more than analysis—but it’s comforting to know that the data is on your side.

If you’re under fire for your beliefs—don’t fold, just get some good information. You don’t need to become the Iron Lady; a fist or two will work in a crunch.

—Rest in peace Prime Minister Thatcher and thank you for showing us how to fight for what we believe.

Excellent Management Systems, Inc.

"The Science Of Success"

John Weathington helps leaders transform organizations.

For over 20 years, John has consulted to people and firms of all sizes including Fortune 500 icons such as Chevron, Hewlett Packard, Sun Microsystems, Wells Fargo, PayPal, Cisco, Pacific Gas and Electric, Hitachi, and Visa where he managed the financial services giant's enterprise data strategy--a program consisting of 150 projects over 45 initiatives and 5 major tracks. Visit John at Excellent Management Systems, Inc. for news, updated information, client results, testimonials, free articles, and more.